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library_name: peft
license: mit
base_model: emilyalsentzer/Bio_ClinicalBERT
tags:
- base_model:adapter:emilyalsentzer/Bio_ClinicalBERT
- lora
- transformers
model-index:
- name: finetunePathologicalTextUsingBioBERT
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetunePathologicalTextUsingBioBERT
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.19.1
- Transformers 5.7.0
- Pytorch 2.6.0+cu124
- Datasets 4.8.5
- Tokenizers 0.22.2 |